TUTOR: Samantha Dawson

LECTURER: Patricia Menéndez

1 Project and Data Description

2 QUESTIONS

2.1 Question1. Which skill category is most common across all Industry Sections and how does it vary between each section?

“The Industry Skills Needs metric captures which skills are most likely to be added to a member’s profile in one industry compared to other industries. It’s calculated using an adapted version of a text mining technique called Term Frequency - Inverse Document Frequency (TF-IDF). This method gives more weight to a skill for an industry if more members in the industry list the skill on their profiles and the skill is more unique to the industry. The skills included are those added while a member holds a particular occupation (i.e. the skill flow approach). While the skill flow approach creates a trade-off whereby long-held basic skills, such as Microsoft Office being given a lesser weight, the approach is shown to be stronger at identifying the latest emerging skills in a specific industry than including all historical skills that are added during prior occupations. On balance, since the objective of this metric is to detect the latest skills needs, a skill flow approach is adopted.” ~2

The most common skill category across different sections is reported as Business Skills.

Table 2.1: Skills and Industry Section
skill_group_category Arts, entertainment and recreation Financial and insurance activities Information and communication Manufacturing Mining and quarrying Professional scientific and technical activities
Specialized Industry Skills 266 5 185 228 39 387
Tech Skills 118 26 307 88 10 205
Soft Skills 83 82 104 151 25 202
Business Skills 32 184 138 215 26 273
Disruptive Tech Skills 1 3 66 18 NA 33
Table 2.1: Top 1 skill category in every industry section
isic_section_name skill_group_category n
Arts, entertainment and recreation Specialized Industry Skills 266
Financial and insurance activities Business Skills 184
Information and communication Tech Skills 307
Manufacturing Specialized Industry Skills 228
Mining and quarrying Specialized Industry Skills 39
Professional scientific and technical activities Specialized Industry Skills 387
count of different skills

Figure 2.1: count of different skills

The TF-IDF text mining technique used in calculation for the skill rank includes the skills that are more unique to one industry than any other. For this reason, for each industry section, Specialized Industry Skills group count is the highest. Specialized Industry Skills are the most common skill category in 4 out of 6 industry sections. However, Information and Communication, has more of Tech Skills, this is owing to the fact that most of the Tech Skills like Microsoft Office are basic across all industries and hence are not categorized as Specialized Industry Skills for ICT. Business Skills(e.g, ) for financial and insurance activities is reasoned in the same manner as above.

percentage of different skills

Figure 2.2: percentage of different skills

The above 100% stacked bar chart 2.2 shows the skill category distribution within each industry section. While 4 out of 6 industry sections have a similar skill category distribution, Financial & Insurance Activities and Arts, Entertainment & Recreation have a rather different skill category distribution. This is because Arts, Entertainment & Recreation is a field in which each talent is a skill and thus Specialized Industry Skills(53%)!!! Financial & Insurance Activities commands Soft Skills and Business Skills(61%). Disruptive Tech Skills are not possessed by members belonging to Mining and quarrying.

Overall,specialized Industry Skills are the most common skill in professional scientific and technical activities. While business skills are the most important for people to acquire in financial and insurance activities.

2.3 For each common skill_category, which industry has the highest penetration rate and what is the change of the common skill penetration rate over the period of time?

“The Skill Penetration metric looks at how many skills from each of LinkedIn’s skill groups (see”Notes" tab) appear among the top 30 skills for each occupation in an industry. For example, if 3 of 30 skills for Data Scientists in the Information Services industry fall into the Artificial Intelligence skill group, Artificial Intelligence has a 10% penetration for Data Scientists in Information Services. These penetration rates are averaged across occupations to derive the industry averages reported. It is likely this metric is best at capturing skill penetration across tradable and knowledge-intensive sectors. For example, it may under-estimate the adoption of AI in. Manufacturing, since LinkedIn members are less likely to be in this sector compared to others." 2

2.3.1 The highest penetration rate for different industry

Table 2.2: Top Industry by Penetration Rate for Each Skill Category
skill_group_name isic_section_name industry_name
Specialized Industry Skills Music Arts, entertainment and recreation Music
Tech Skills Graphic Design Professional scientific and technical activities Graphic Design
Business Skills Insurance Financial and insurance activities Insurance
Soft Skills Writing Information and communication Writing & Editing
Disruptive Tech Skills Development Tools Information and communication Computer Software

The table 2.2 demonstrates that music industry has the highest skill penetration rate for skill groups among all industries.

Figure 2.6: The penetration rate for different industry

The figure 2.6 shows that music industry ranks first with the penetration rate of 25% on average in a time span of five years, graphic design ranked the second (22%), insurance ranked the third(9%) and writing & editing ranked the third (8%). In addition, aviation & aerospace ranked last in 2015(3%), but were replaced by computer software(5%) in the following years.

2.3.2 The change of the common skill penetration rate

Figure 2.7: Change for skill peneration rate

The figure 2.7 deceits that specialized industry skills ranking first which peaked at 27% in 2017 had fluctuation during the time period while tech skills(2nd, average 22%) keep steadily. And soft skills had a slight climb until exceeding business skills in mid-2017(8%) which had a downward trend. However, disruptive tech skills ranks the last with 5% on average.

2.3.3 Conclusion

From the two figures, it is not hard to find that if workers want to enter an industry with a strong professional field, they are often required to master a unique and high threshold skill, such as music and graphic design. While industries with low technical penetration may require more alternative skills due to the fragmentation of the industry. Therefore it is true that the skill penetration rate of an industry is effected by various factors such as the degree of internal segmentation of the industry and the industry itself.

In general, hundreds of skills can be categorized by five common skills. Undoubtedly the specialized industry skills and tech skills has the higher rate which meet the requirements of industry development. Interestingly, with the advent of the era of big data and technology, the importance of many traditional skills like business skill has gradually declined, as shown in the decreasing penetration rate, which means that they are more replaceable in the industry and therefore no longer unique.

2.4 Find the industry_section that is best to each region/continent.

The employment growth data represents the overall rate of change of employment between a pair of consecutive years for an industry, across 2015 to 2019. This rate of change is called “growth rate” which is measured by the percentage change in the number of employees for that industry .The sample of LinkedIn members is limited to those that have a company registered on LinkedIn on their profile. For any year, the number of employees working in an industry is the cumulative sum of the shift in the employed industry of the LinkedIn members, that is, the sum of the linked profiles with no shift in industry and the difference of the number of employees entering that industry and the number of employees leaving that industry. For example, if an industry has for the year 2014, 1000 employees and 100 employees enter this industry and 50 employees leave this industry in 2015, then the number of employees in this industry for the year 2015 is 1000 + 100 - 50 and the growth rate in 2015 is 0.05%. The formula for growth rate is, \[\begin{equation} growth rate = (membercount_i-membercount_j/membercount_i)*100 \end{equation}\]

The growth is described with respect to variables such as, country_code, country_name, wb_region, wb_income, isic_section_index, isic_section_name, industry_id, industry_name, year, and growth_rate. However, the original data is in a wider format with growth rates for different years represented in the same row. As such, in accordance with the tidy data definition, the data was transformed into longer format and hence cleaned. For the question, records are filtered for the appropriate industry section for each region and then analysis is done for industries within that section.

2.4.1 Industry Sections: Highest and Lowest Average Growth Rate

The average growth rate is calculated for industry sections in each region and the industry sections with highest and lowest average growth rate over all the years is reported in Table 2.3 and Table 2.4 respectively.

Table 2.3: Industry Sections: Highest Average Growth Rate
Region Industry_section Avg_growth_rate
East Asia & Pacific Financial and insurance activities 0.026
Europe & Central Asia Financial and insurance activities 0.013
Latin America & Caribbean Financial and insurance activities -0.003
Middle East & North Africa Mining and quarrying 0.008
North America Financial and insurance activities 0.026
South Asia Manufacturing -0.006
South Asia Mining and quarrying -0.006
Sub-Saharan Africa Manufacturing 0.006
Table 2.4: Industry Sections: Lowest Average Growth Rate
Region Industry_section Avg_growth_rate
East Asia & Pacific Mining and quarrying 0.002
Europe & Central Asia Professional scientific and technical activities 0.002
North America Mining and quarrying 0.001
Sub-Saharan Africa Information and communication -0.009
Latin America & Caribbean Information and communication -0.016
Middle East & North Africa Information and communication -0.017
South Asia Information and communication -0.019

2.4.2 Industries and Regions: Analysis

However, owing to several factors such as environmental resources, workforce and infrastructure, the economies of regions are dependent on specific industries. For example, Middle East & North Africa have huge fossil fuel deposits, and hence excel in Oil and Mining, while North America has the infrastructure and workforce for Information and communication industries. The Table 2.5 shows the the industry sections and the regions in which they had highest growth.

Deeper Insights can be made from 2.9 and 2.10

Table 2.5: Region: Industry Sections
Industry_Section Region Avg_growth_rate
Financial and insurance activities East Asia & Pacific 0.026
Arts, entertainment and recreation East Asia & Pacific 0.008
Mining and quarrying Europe & Central Asia 0.008
Mining and quarrying Middle East & North Africa 0.008
Financial and insurance activities North America 0.026
Information and communication North America 0.022
Professional scientific and technical activities North America 0.014
Manufacturing North America 0.013

The presence of industries in the regions is shown in Figure 2.8.

Presence of industries in each region

Figure 2.8: Presence of industries in each region

  • East Asia & Pacific, North America and Europe & Central Asia have been growing in terms of employment with Financial and insurance activities being the most significant employer.

  • Industries in South Asia and Latin America & Caribbean had only contraction, with industries under the section Manufacturing and Mining and quarrying being the least affected. In Sub-Saharan Africa other than Manufacturing all other industries have been declining in terms of employment.

  • Information and communication has been contracting in Sub-Saharan Africa, Latin America & Caribbean, Middle East & North Africa and South Asia which otherwise has a tremendous scope in North America.

  • North America, East Asia & Pacific, and Europe & Central Asia are the regions where all industries upgraded.

  • North America has been the leader in all Financial and insurance activities,
    Information and communication, Professional scientific and technical activities, Manufacturing whose biggest competitor is East Asia & Pacific.

  • Mining and quarrying, however, retains a strong position in Middle East & North Africa.

Region: Industry Sections

Figure 2.9: Region: Industry Sections

Industry Sections: Region

Figure 2.10: Industry Sections: Region

2.5 Which industry_name sees the maximum growth over time in each region/continent , depending on the region’s best industry_section ?

The selection of an industry section for a region is based on the Table 2.3. A comprehensive analysis is made on the industries that fall in these industry sections. The number of industries in each industry section is given in Figure 2.11

Industry Count within each Section

Figure 2.11: Industry Count within each Section

The regions North America, East Asia & Pacific, and Europe & Central Asia have a similar distribution of the growth rates for industries in Financial and insurance activities. Industries relating to investments have a growth rate[0.03,0.05] far exceeding other industries within this field. Banking, however remained in place. It is interesting to note that in the Middle East, Oil and Energy saw a decline. These comparisons have been made in Figure 2.12. The aggregated growth rate for each region is made in the time series graphs 2.14 .

Avg. growth of an industry within a region w.r.t best industry section

Figure 2.12: Avg. growth of an industry within a region w.r.t best industry section

Each of the time series graphs below represents the cumulative averages for the growth rates of industry sections. The regions having the same industry sections are compared in each graph. The growth rate for Mining and quarrying in South Asia has been declining below whereas in Middle East & North Africa it has seen a steady growth . North America and East Asia & Pacific are close competitors in Financial and insurance activities with North America beating East Asia & Pacific in the recent times. The growth rate for Manufacturing is a similar trend as the Mining and quarrying where steady growth is observed in Sub-Saharan Africa.

The trend of industries within each section is represented in the Figure 2.15.

Figure 2.13: Time Series: Aggregated Growth Rate

Figure 2.13: Time Series: Aggregated Growth Rate

Figure 2.13: Time Series: Aggregated Growth Rate

Figure 2.14: Time Series: Aggregated Growth Rate

Figure 2.15: Time Series: Industry Growth Rate

2.5.1 Discussion on Skills and Industries

The presence of skill categories in industry sections is shown in Figure 2.16 and the count of skills within each skilll category is displayed. Specialized Industry skills have a very large difference as compared to other skills owing to the fact that these skill categories are most unique in an industry. Business skills have a presence in many industries.

skill categories in industry sections

Figure 2.16: skill categories in industry sections

2.5.1.1 Relationship between Skill Group Rank, Industry Growth Rate and Skill Group Pentration Rate

There exists no relationship between skill group rank and skill group penetration rate and for some industries, penetration rate is higher where there is no growth or little growth, thus suggesting that employees incorporate more skills. No relationship is determined.

Skill Group Rank vs Skill Group Pentration Rate

Figure 2.17: Skill Group Rank vs Skill Group Pentration Rate

Industry Growth Rate and Skill Group Pentration Rate

Figure 2.18: Industry Growth Rate and Skill Group Pentration Rate

2.5.2 Networks: Industry Sections and Skill Groups

The network below 2.19 shows the relationship between industry sections and skill categories wighted by the mean rank of these skills. Specialized Industry Skills have the highest rank across all industries. However, Financial and insurance activities demand more of Business skills. Business skills have a fair rank across industries. Tech skills and soft skills are ranked well for all industries; tech skills are more important to Information and communication whereas soft skills are important to manufacturing. Disruptive tech skills are however ranked highly only for Information and communication, manufacturing and professional, scientific and technical activities. It is to be noted that the count of combination of industry sections and skill categories in the observations(Figure 2.16) gives a similar result as the ranked network analysis.

Detailed network of industries and skills weighed by the skill group ranks for every industry section is present in subsequent graphs.

Network: Industry Section and Skill Category

Figure 2.19: Network: Industry Section and Skill Category

Mining and quarrying

Figure 2.20: Mining and quarrying

Manufacturing

Figure 2.21: Manufacturing

Information and communication

Figure 2.22: Information and communication

Financial and insurance activities

Figure 2.23: Financial and insurance activities

Professional scientific and technical activities

Figure 2.24: Professional scientific and technical activities

Arts, entertainment and recreation

Figure 2.25: Arts, entertainment and recreation

2.5.3 Networks: Migration

Migration rate is the net flows(arrivals - departures) normalized based on the member count in the target country multiplied by 10000. A positive migration is when the arrivals are greater than the departures and vice-versa. The migration rate for the countries averaged over all industries and years is shown in the map 2.26

Table 2.6: Top Countries for Migration
country_name average_migration_rate
Luxembourg 765.3817
United Arab Emirates 442.7116
Malta 396.6229
Estonia 347.1595
Cyprus 342.0833
Qatar 332.0523
Panama 283.6780
Myanmar 258.0705
Kuwait 237.3493
Mali 237.1740
Switzerland 233.6345
Burkina Faso 220.4540
Saudi Arabia 208.4615
New Zealand 197.5190
Bahrain 195.1179
Ireland 182.0494
Singapore 178.8927
Rwanda 175.4360
Germany 171.9108
Papua New Guinea 169.6560
Japan 168.5637
Congo, Dem. Rep.  161.9225
Zambia 151.8496
Georgia 150.8675
Australia 142.9156
Austria 137.7601
Canada 133.5462
Chile 119.2267
Czech Republic 118.9811
Thailand 115.2884
Map: Migration Rate of Countries

Figure 2.26: Map: Migration Rate of Countries

A network depicting the highest migration rate for a base country in shown below. This means the highest number of people that migrated to a country. The network is weighted on the average migration rate over the years. The two major clusters, the United States and India suggest that most most of people from most countries migrate to the United States of America. However,for India these might be the returning people who migrated a few years ago to the base countries. We can also see that the migration linkage is also dependent on the geographical and historical ties of the countries. For example, Venezuela is target country for the countries in Latin America and Caribbean, Hong Kong to China, West Bank and Gaza to Israel.

Highest Migration Rate Selected: Base Country to Target Country

Figure 2.27: Highest Migration Rate Selected: Base Country to Target Country

2.6 For each region, which country did the above found industry had had maximum growth? And, what is the income group of that nation?

2.6.1 What is the Avg. growth of the best industry within a country w.r.t ts best industry section?

Figure 2.28: Avg growth of the best industry within in a country w.r.t region

The figure 2.28 take us further step by adding another layer of Country to the ??.

  • Mostly every region had a big top knot Country baving the max growth rate of an employee, whereas regions like South Asia and Sub-Saharan Africa had countries like Nepal and Zambia having the maximum growth rate even though coming under Low/Lower middle income categories.
  • Though overall North America had the max growth of employee in the Venture Capital & Private Equity (see Figure ??), but when seen country wise, Luxembourg in Europe & Central Asia region had approximately double the growth than top country Canada .

2.6.2 What is the trend of the best industry within a country w.r.t ts best industry section?

Figure 2.29: Trend of best industry within in a country w.r.t region

The figure 2.29 provides a broader insight to the figure 2.28 by showing the trend of grqwth in each Country in a span of 5 years from 2015-2019.

Mostly every country showed s steady graph with some exceptions like:

  • In Latin America & Caribbean, Chile saw a constant drop in ts growth rate whereas Venezuela showed an opposite behaviour.
  • In Miiddle East & North Africa region, Kuwat though still topping the Avg.growth rate over the years, yet saw a massive fall in its growth rate.

3 Conclusion

4 References

4.2 Bibliographies

R Core Team (2021)

R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Appendix

Country – countries with 100,000+ LinkedIn members.
World Bank Region – countries as classified given the most recent 6 regional World Bank country categories.
World Bank Income Group – countries are classified given the most recent World Bank country classification by GNI into 4 categories: Low Income, Lower Middle Income, Upper Middle Income, and High Income.
Industry – Detailed economic activity defined through the LinkedIn industry classification (approximately ISIC Rev. 4 2 digit level), covering approximately 140 industries (industries may be excluded based on data quality considerations) which compose the six ISIC Rev. 4 tradable sectors (ISIC Index: B, C, K, J, M, R). Please see LinkedIn – ISIC industry mapping file https://datacatalog.worldbank.org/node/144635
ISIC Section – The LinkedIn industry taxonomy is mapped to ISIC Rev. 4 Sector (1 digit) categories. Data is limited to 6 tradable sectors (ISIC Index: B, C, K, J, M, R). Please see LinkedIn – ISIC industry mapping file. https://datacatalog.worldbank.org/node/144635
Tradable and Knowledge-Intensive Sectors – Six knowledge-intensive and tradable sectors, using ISIC Rev. 4 classification, are: B-mining and quarrying; C-manufacturing; J-information and communication; K-financial and insurance activities; M-professional, scientific, and technical activities; and R-arts, entertainment and recreation.
Industry Skills Needs – Captures the most-distinctive, most-represented skills of LinkedIn members working in a particular industry. Based on the skills section of the LinkedIn profile. It’s calculated using an adapted version of a text mining technique called Term Frequency - Inverse Document Frequency (TF-IDF).
Skill Penetration – Measures the time trend of a skill across all occupations within an industry. Based on skill addition rates, and the number of times a particular skill appears in the top 30 skills added across all of the occupations within an industry. For example, if 3 of 30 skills for Data Scientists in the Information Services industry fall into the Artificial Intelligence skill group, Artificial Intelligence has a 10% penetration for Data Scientists in Information Services. These penetration rates are averaged across occupations to derive the industry averages reported.
Migration Overview – All the metrics are based on net migration (arrivals minus departures). These net migration figures are each normalized differently to enable fairer comparisons across samples. We calculate all on an annual basis, and report an average of the last three years.
Industry Migration – Industries gained and lost. Based on the industry associated with a member’s company at the time of migration. The net gain or loss of members from another country working in a given industry divided by the number of LinkedIn members working in that industry in the target (or selected) country, multiplied by 10,000.
Industry Employment Shifts – Captures the transitions among industries over time by LinkedIn members as a proxy for industry employment growth. Based on the industries declared by the companies in a member’s work history.